CN105956281A - Charging design method of solid rocket engine - Google Patents
Charging design method of solid rocket engine Download PDFInfo
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Abstract
The invention provides a charging design method of a solid rocket engine. An agent model is used as a basis to directly carry out approximation on a thrust curve, and the thrust curve, instead of the agent model of a least square deviation between the thrust curve and a design index, is constructed to effectively describe a change rule of the thrust along with time so as to obviously reduce the simulation frequency of a high-precision burning surface and a trajectory. The agent model can better describe the change rule of the thrust curve, effectively guides subsequent search in a subsequent design, and can obviously improve the charging design efficiency of the solid rocket engine.
Description
Technical field
The present invention relates to aircraft engine design field, be specifically related to a kind of solid propellant rocket motor charge method for designing.
Background technology
Solid propellant rocket is one of important motivity system of the space launch vehicle such as guided missile, rocket.The powder charge design of solid propellant rocket, typically requires under meeting electromotor internal ballistics attributes and relevant constraint, selects Types of Medicine and determine its geometric parameter, considering the design requirement of burning chamber shell internal insulation layer, lining and artificial soil rapid filter simultaneously.Powder charge design is the technology that Design of Solid Propellant Rocket Engine is most crucial.Space shuttle solid booster rocket engine has the features such as big L/D ratio, higher axial pressure drop, erosive bruning ignition process serious, complicated, the distribution design requirement of powder charge manufacture process hump effect uncertain and less internal ballistics attributes, needs to use advanced modeling method raising inner trajectory.
The main task of powder charge design is the geometric configuration (i.e. geometric parameter) by adjusting powder charge, and the thrust making powder charge produce in combustion meets the thrust requirements that engine total design proposes.
See Fig. 1, after the geometric configuration of given powder charge, the thrust curve of correspondence can be obtained by running Geometric Modeling, burning area calculation and inner trajectory emulation, if thrust curve meets overall objective, then export design result (motor charge geometric parameter and thrust curve), otherwise it is necessary to use certain adjustable strategies to find next geometric parameter.
The most conventional charge disign method has:
(1), based on existing case and experience, after manually adjusting powder charge geometric parameter, it is iterated search.This type of method in the industrial production most, because production division is engaged in powder charge and produces and design throughout the year, have accumulated a large amount of practical operation experience and case, therefore can select by preferably initial to iteration geometric parameter, thus the accuracy of gained data of explosive filled after improving design.This type of method be only limitted to experienced engineer participate in design in the case of, could use, and craft iteration inefficient；
(2) Optimization Design is used for powder charge design, the optimization problem of structure powder charge design, automatically searches for optimization method.This type of method can avoid manual iteration, and need not too many engineering experience.Li Xiaobin, Zhang Weihua, Wang Zhongwei is published in Optimization Design disclosed in the article of " Push Technology " in April, 2006 volume 27 the 2nd interim " powder charge geometric parameter uncertainty optimization design ", is i.e. the processing method being optimized powder charge geometric parameter by Optimization Design.It is as follows that such as this type of method implements step:
A, set up Optimized model
First, set up the parameterized model of powder charge configuration, determine design variable, optimization aim and bound variable.It is commonly designed the geometric parameter that variable is powder charge, the geometric configuration of powder charge can be uniquely determined by these parameters, it is minimum that optimization aim designs the deviation between gained inner trajectory curve and the inner trajectory curve of design objective requirement for simulation optimization, and bound variable is the performance indications (such as: the indexs such as total punching, mass ratio, the logical ratio of larynx) of electromotor.
B, selection optimization method
In the selection of optimization method, generally using intelligent optimization method to combine with local search approach, this type of method, without substantial amounts of iterative computation, uses the analytic method of low precision to carry out combustion face and retires rule emulation.Or employing optimization method based on agent model combines with combustion face phantom and is optimized, the simulation result precision of this model is higher, and the search efficiency of this optimization method is higher.
This type of method can avoid relying on experience and be designed, and uses manual iteration, but all kinds of optimized algorithms owing to being used are both needed to carry out substantial amounts of simulation calculation, therefore can only be by the burning area calculation method resolved, it is impossible to that accurately portrays combustion face retires rule.Even if using agent model and high accuracy combustion face to retire the method for designing of rule, it is still desirable to carrying out combustion face and the inner trajectory emulation of up to a hundred times, calculation cost is the biggest.And thrust curve is converted into a scalar and is optimized by the method using optimization to carry out powder charge design at present, various thrust curve form often obtains an identical index, brings difficulty to optimizing.
Summary of the invention
It is an object of the invention to provide a kind of solid propellant rocket motor charge method for designing, this invention solves solid propellant rocket motor charge design process inefficiency in prior art, excessively relies on the technical problem of experience.
The invention provides a kind of solid propellant rocket motor charge method for designing, comprise the following steps:
1) the thrust requirements curve F of given motor charge Geometric configuration design index request0(t)；
2) set up the parameterized model of motor charge geometric configuration, determine design variable X and the scope thereof of required process according to the type of handled powder charge geometric configuration；
3) number m and scope thereof according to design variable X set up design space, optimum Latin Hypercube Sampling method is used to gather 2m sampled point in design space, performance simulation model is set up in each sample point, and runnability phantom, obtain the 2m bar thrust curve that each sampled point is corresponding, gained design variable X value X at ith sample pointiAnd the emulation thrust curve f corresponding from different sampled pointsiT the corresponding relation between (), as shown in formula (1)；
Wherein, XiFor design variable X thrust magnitude at ith sample point, fiT () is for being designed emulating, to the different each design variables of sample point, the emulation thrust curve obtained respectively, by the most discrete for N number of point for 2m bar emulation thrust curve difference on some t of each working time, obtaining the sample set as shown in formula (2), this sample set represents each discrete instants tiThrust magnitude f2m(tN) and design variable XiBetween corresponding relation:
4) the agent model s of each discrete instants thrust is constructed according to formula (2)i(X), N number of agent model, simultaneously s are obtainedi(X) formula (3) is met:
F(ti)=si(X) (3)
Wherein, F (ti) it is discrete instants tiCorresponding thrust, wherein tiIn i meet 1 < i < N；
si(X) it is according to sample data [Xj,fj(ti)] wherein j=1,2 ..., 2m+k, each discrete point constructs the thrust agent model obtained；
5) according to gained N number of thrust agent model, the optimization problem shown in solution formula (4), obtaining optimal solution corresponding to this optimization problem is design variable optimal solution Xk+2m；
In gained design variable optimal solution Xk+2mPlace's runnability phantom, obtain the optimum thrust curve of correspondence, and by optimum thrust curve on point of each working time discrete for N number of point, add in matrix (2), then the sample point number in formula (2) becomes 2m+k from N number of；
6) convergence judges: when iteration is initial, make iterations k=0, it is intended that search precision eps and maximum search step number Kmax, the mean-squared departure of any two the most corresponding thrust curves of different discrete instants it is iterated being calculated according to formula (5):
Wherein, F2m+k-1(ti) it is the thrust curve at the 2m+k-1 discrete point, F2m+k(ti) it is the thrust curve at the 2m+k discrete point, F0(ti) it is the thrust curve of design objective requirement, N is discrete point number；
If error (k) is ＜ eps or k=Kmax, then stop search, export design variable optimal solution Xk+2mAnd the optimum thrust curve f of correspondence2m+k(t), [X2m+k,f2m+k(t)], otherwise, go to step 4) until iteration stopping when meeting this condition.
Further, search precision eps is appointed as 0.001.
Further, maximum search step number KmaxIt is appointed as 5m.
The technique effect of the present invention:
The present invention provides solid propellant rocket motor charge method for designing
1, the present invention provides solid propellant rocket motor charge method for designing, based on agent model, directly thrust curve is approximated, agent model by the least square deviation between structure thrust curve rather than thrust curve and design objective, effectively depict thrust rule over time, thus considerably reduce high accuracy combustion face and the number of times of inner trajectory emulation, design for solid propellant rocket motor charge and provide method for designing fast and accurately.
2, the present invention provides solid propellant rocket motor charge method for designing, owing to the thrust of multiple discrete instants is carried out approximate modeling, therefore the consideration to thrust curve is the finest, the iterations that obtaining optimal solution needs reduces at least one order of magnitude than existing method only needs 15～40 iteration i.e. to can get preferred value, it is achieved thereby that the quick design to powder charge.
3, the present invention provides solid propellant rocket motor charge method for designing to improve Design of Solid Propellant Rocket Engine automaticity, and artificial participation process reduces so that it is depend on the experience of engineer not too much.
4, the present invention provides solid propellant rocket motor charge method for designing execution efficiency height, desin speed fast, makes the most time-consuming powder charge in electromotor design be designed to carry out automatic Iterative, greatly reduces design time-consuming.
Specifically refer to the described below, by apparent for the above and other aspect making the present invention of the various embodiments that solid propellant rocket motor charge method for designing according to the present invention proposes.
Accompanying drawing explanation
Fig. 1 is motor charge Geometric configuration design method flow schematic diagram in prior art；
Fig. 2 is the solid propellant rocket motor charge method for designing schematic flow sheet that the present invention provides；
Fig. 3 is rear wing column type powder charge configuration schematic diagram used in the preferred example of the present invention 1 and 2, a) is that the master of rear wing column type powder charge configuration regards cross-sectional schematic, b) is the schematic side view of rear wing column type powder charge configuration；
Fig. 4 is the least square deviation monitored results schematic diagram of the preferred example of the present invention 1；
Fig. 5 is the single trust engine design result schematic diagram of the present invention preferred example 1；
Fig. 6 is the present invention preferred example 2 least square deviation monitored results schematic diagram；
Fig. 7 is the present invention preferred example 2 dual-thrust motor design result schematic diagram.
Detailed description of the invention
The accompanying drawing of the part constituting the application is used for providing a further understanding of the present invention, and the schematic description and description of the present invention is used for explaining the present invention, is not intended that inappropriate limitation of the present invention.
For ease of understanding, method provided by the present invention is summarized as follows: the method for designing that the present invention provides first by discrete for corresponding for general requirement each thrust curve for several points, construct agent model by each discrete point, depict the time dependent process of thrust.During search optimal solution, it is considered to each discrete point approximation ratio to design objective, update agent model by reducing the deviation between each discrete point and design objective.Thus reduce the iterations obtained needed for optimal solution.Improve iteration efficiency.
The method for designing that the present invention provides is suitable to process the emulation of all kinds of inputs, input herein includes: powder charge geometric configuration phantom, combustion face passage phantom and inner trajectory phantom, be referred to as performance simulation model by required input in design process herein.
Seeing Fig. 2, the present invention provides a kind of solid propellant rocket motor charge method for designing, comprises the following steps:
1) the thrust requirements curve F of given motor charge Geometric configuration design index request0(t)。
Motor power curve is the input preset during electromotor design herein, is the rocket master-plan property indices requirement that proposes electromotor, thrust curve F0T () is a known curve.
2) set up the parameterized model of motor charge geometric configuration, determine design variable X and the scope thereof of required process according to the type of handled powder charge geometric configuration；
In available engine design process, the geometric configuration of powder charge has " cartwheel pattern ", " star pass ", " cabane type " etc. multiple, each configuration has corresponding profile to control parameter index, and the method that the present invention provides is applicable to the powder charge mechanism of existing all kinds of configuration.
3) number m and scope thereof according to design variable X set up design space, use optimum Latin Hypercube Sampling method to gather 2m sampled point, set up performance simulation model, and runnability phantom in each sample point in design space；
Obtain the 2m bar thrust curve that each sampled point is corresponding, gained design variable X value X at ith sample pointiAnd the emulation thrust curve f corresponding from different sampled pointsiT the corresponding relation between (), as shown in formula (1)；
Wherein, XiFor design variable X thrust magnitude at ith sample point, fiT () is for being designed emulating, to the different each design variables of sample point, the emulation thrust curve obtained respectively, by the most discrete for N number of point for 2m bar emulation thrust curve difference on some t of each working time, obtaining the sample set as shown in formula (2), this sample set represents each discrete instants tiThrust magnitude f2m(tN) and design variable XiBetween corresponding relation:
4) the agent model s of each discrete instants thrust is constructed according to formula (2)i(X), N number of agent model, simultaneously s are obtainedi(X) formula (3) is met:
F(ti)=si(X) (3)
Wherein, F (ti) it is discrete instants tiThe thrust that (1 ＜ i ＜ N) is corresponding；
Discrete instants t can be predicted by formula (3)iThrust F (the t of (1 ＜ i ＜ N)i) and design variable X between relation.si(X) it is according to sample data [Xj,fj(ti)] (j=1,2, ..., 2m+k), constructing the thrust agent model obtained on each discrete point, agent model building method sees herein: the thesis of Nanjing Aero-Space University's graduation master Mu's snowy peak in 2004: " research of multidisciplinary design optimization agent model technology and application ".
5) according to the N number of agent model of gained, the optimization problem shown in solution formula (4), obtaining this optimization problem optimal solution is design variable optimal solution Xk+2m；
In gained design variable optimal solution Xk+2mPlace's runnability phantom, obtain the optimum thrust curve of correspondence, and by optimum thrust curve on point of each working time discrete for N number of point, add in matrix (2), then the sample point number in formula (2) becomes 2m+k from N number of；
6) convergence judges: when iteration is initial, make iterations k=0, it is intended that search precision eps and maximum search step number Kmax, it is iterated being calculated the mean-squared departure of any two the most corresponding thrust curves of different discrete instants afterwards according to formula (5):
Wherein, F2m+k-1(ti) it is the thrust curve at the 2m+k-1 discrete point, F2m+k(ti) it is the thrust curve at the 2m+k discrete point, F0(ti) it is the thrust curve of design objective requirement, N is discrete point number；
If error (k) is ＜ eps or k=Kmax, then stop search, export design variable optimal solution Xk+2mAnd the optimum thrust curve f of correspondence2m+k(t), [X2m+k,f2m+k(t)], otherwise, go to step 4) until iteration stopping when meeting this condition.
Preferably, search precision eps is appointed as 0.001.Preferably, maximum search step number KmaxIt is appointed as 5m.By can effectively reduce iterations during this value.
F (t) is thrust curve, and it has been become after discrete the thrust magnitude in N number of moment.Formula (5) illustrates the mean-squared departure of any two the most corresponding thrust curves of different discrete points, reflect the degree of closeness of these two thrust curves, if its meaning is in twice iteration the optimum thrust curve obtained fairly close (mean-squared departure is less than predetermined precision), then can determine that calculating convergence.Thus terminate iteration, and the thrust curve that output design variable optimal solution is corresponding simultaneously, obtain desired result, iterations control effectively simultaneously and reduce.By repeated multiple times iterative computation, realize being modified by agent model calculated optimal solution thrust curve, when this optimal solution thrust curve revised can meet end condition till, it is achieved thereby that the correction of thrust curve calculated to agent model.
In the method, the structure of performance simulation model used refer to [Dong Shiyan, Zhang Zhaoshun. " solid propellant rocket principle ". publishing house of Beijing Institute of Technology], [side fourth tenth of the twelve Earthly Branches, Zhang Weihua, Tao Yang. " internal ballistics of solid rocket motors ". publishing house of the National University of Defense technology].
After below as a example by cabane h type engine h powder charge design, method is provided to illustrate the present invention:
In example 1～2, design parameter is: single thrust (constant value thrust) and dual thrust (piecewise constant thrust) electromotor are the most extensive.This section, as a example by single thrust and dual-thrust motor, provides two case study on implementation, powder charge formula and datum configuration and uses same form.
Powder charge attribute:
Burning rate coefficient 0.0765, Pressure Exponent 0.34, characteristic velocity 1550, density 1700, combustion gas specific heat ratio 1.2
In following example 1～2, the basic configuration of rear wing column type powder charge configuration used mostly is the rear cabane powder charge using 8 wings, and its geometric configuration and required design variable all represent in figure 3, and in following example 1～2, required design variable and span thereof are as shown in table 1.
Table 1 rear wing column type powder charge design variable and range table thereof
The solid propellant rocket motor charge method for designing that in example 1～2, the present invention used provides, comprises the following steps:
1) Latin hypercube design method optional 10 sample points in design space (the most each variable-value scope composition) are used, in these 10 sample points can be a certain range of variables, it is also possible to for choose in multiple ranges of variables.Run motor charge geometric simulation model, obtain a plurality of thrust curve, and calculate the mean-squared departure of each thrust curve and thrust requirements the most respectively:
Wherein, T is engine operating duration, F0T () is thrust requirements curve, f (t) is gained thrust curve, f0For average thrust；This formula is a kind of general mean-squared departure identification method.Formula (6) herein is all in example use, is to use when showing result of calculation intuitively and be monitored the intermediate parameters of design process.
2) by each thrust curve operationally between the most discrete on T be 20 discrete time points (i.e. N=20)；
3) according to 10 initial sample points, 20 discrete time points construct thrust agent model respectively；
4) use the optimization problem shown in adaptive differential evolution algorithm solution formula (4), obtain design variable optimal solution Xk+2m, in design variable optimal solution Xk+2mPlace runs powder charge geometric configuration phantom, obtains the thrust curve that optimal solution is corresponding, and calculates the mean-squared departure of this thrust curve and thrust requirements according to formula (5)；
6) stop technology: if meeting end condition, then output agent model calculated optimal solution thrust curve, corresponding optimal solution thrust curve, otherwise, the data of optimum solution point are added step 1) in sample set in, carry out next step iteration, by repeated multiple times iterative computation, realize being modified by agent model calculated optimal solution thrust curve, till when this optimal solution thrust curve revised can meet end condition, it is achieved thereby that the correction of thrust curve calculated to agent model.
Example 1:
Subjects: single trust engine
Design objective:
Thrust 60kN, working time 5s, powder charge external diameter 291mm, powder charge internal diameter 91mm, packing factor 0.8, charging quality 125kg
Design process mean-squared departure as shown in Figure 4, thrust curve corresponding to lowest mean square deviation as shown in Figure 4, the design result that required each design variable and each variable use the present invention to obtain after providing method for designing design is shown in Table 2.
According to above-mentioned steps solves mean-squared departure, after obtaining mean-squared departure, it is carried out record every time, be curve shown in Fig. 4.Design after terminating in all calculated thrust curves, select the thrust curve that mean-squared departure is minimum, be the thrust curve that mean-squared departure in Fig. 4 is corresponding, be listed in Fig. 5.
The design result table of table 2 example 1
Example 2:
Subjects: dual-thrust motor
Design objective:
One-level thrust 150kN, working time 1s, transit time 1s, two grades of thrusts 60kN, working time 3s, powder charge external diameter 291.7mm, powder charge internal diameter 117mm
Design process mean-squared departure as it is shown in figure 5, thrust curve corresponding to lowest mean square deviation as shown in Figure 6, the design result that corresponding each design variable and each variable use the present invention to obtain after providing method for designing design is shown in Table 3.
According to above-mentioned steps solves mean-squared departure, after obtaining mean-squared departure, it is carried out record every time, be the curve shown in Fig. 6.Design after terminating in all calculated thrust curves, select the thrust curve that mean-squared departure is minimum, be the thrust curve that mean-squared departure in Fig. 6 is corresponding, be listed in Fig. 7.
The design result table of table 3 example 2
Seeing Fig. 4 to understand, the method that the present invention provides only needs to carry out calling of 21 High Precision Simulation models and i.e. can get iteration result, and amount of calculation is greatly reduced.Referring also to Fig. 5, the result goodness of fit that gained thrust requirements is to be reached with design is higher, illustrates that the method for designing acquired results provided according to the present invention is preferable.
And using existing based on intelligent optimization or agent model Optimization Design, the calculation times of needs is far longer than the number of times needed for the present invention.The iterations statistics that distinct methods needs is as shown in table 4.
Table 4 example 1～2 and existing method for designing solve simulation times result table needed for design result
[1] Wu Zeping. change propulsive solid propellant rocket motor charge method for designing. the annual meeting in 2015 of rocket engine Professional Committee of power branch of China Aviation association, enshi, 2015
[2]K.M.Albarado,R.J.Hartfield,B.W.Hurston,R.M.Jenkins,Solid Rocket Motor Performance Matching Using Pattern Search/Particle Swarm Optimization,47th AIAA/ASME/SAE/ASEE Joint Propulsion Conference,San Diego,California,2011.
As shown in Table 4, the method for designing that the present invention provides is in the iteration call number of phantom, at least few than existing method for designing an order of magnitude, fully demonstrates energy effectiveness of the present invention and reduces iterations required in design engine configuration parametric procedure, be conducive to improving design efficiency.
Those skilled in the art will understand that the scope of the present invention is not restricted to example discussed above, it is possible to it is carried out some changes and amendment, the scope of the present invention limited without deviating from appended claims.Although oneself is through illustrating and describing the present invention the most in detail, but such explanation and description are only explanations or schematic, and nonrestrictive.The present invention is not limited to the disclosed embodiments.
By to accompanying drawing, the research of specification and claims, it will be appreciated by those skilled in the art that and realize the deformation of the disclosed embodiments when implementing the present invention.In detail in the claims, term " includes " being not excluded for other steps or element, and indefinite article " " or " a kind of " are not excluded for multiple.The fact that some measure quoted in mutually different dependent claims do not mean that the combination of these measures can not be advantageously used.Any reference marker in claims is not construed to limit the scope of the present.
Claims (3)
1. a solid propellant rocket motor charge method for designing, it is characterised in that comprise the following steps:
1) the thrust requirements curve F of given motor charge Geometric configuration design index request0(t)；
2) set up the parameterized model of motor charge geometric configuration, determine according to the type of handled powder charge geometric configuration required
The design variable X processed and scope thereof；
3) number m and scope thereof according to design variable X set up design space, use optimum Latin to surpass in design space
Cube sampling method gathers 2m sampled point, sets up performance simulation model, and runnability phantom in each sample point,
Obtain the 2m bar thrust curve that each sampled point is corresponding, gained design variable X value X at ith sample pointiAnd with not
With the emulation thrust curve f that sampled point is correspondingiT the corresponding relation between (), as shown in formula (1)；
Wherein, XiFor design variable X thrust magnitude at ith sample point, fiT () is for respectively designing change to different sample point
Amount be designed respectively emulating the emulation thrust curve obtained, by 2m bar emulation thrust curve each working time point t on minute
The most discrete for N number of point, obtain the sample set as shown in formula (2), this sample set represents each discrete instants tiThrust
Value f2m(tN) and design variable XiBetween corresponding relation:
4) the agent model s of each discrete instants thrust is constructed according to formula (2)i(X), N number of agent model is obtained, simultaneously
si(X) formula (3) is met:
F(ti)=si(X) (3)
Wherein, F (ti) it is discrete instants tiCorresponding thrust, wherein tiIn i meet 1 < i < N；
si(X) it is according to sample data [Xj,fj(ti)] wherein j=1,2 ..., 2m+k, each discrete point constructs and obtains
Thrust agent model；
5) according to gained N number of thrust agent model, the optimization problem shown in solution formula (4), this optimization problem pair is obtained
The optimal solution answered is design variable optimal solution Xk+2m；
In gained design variable optimal solution Xk+2mPlace's runnability phantom, obtains the optimum thrust curve of correspondence, and will
Optimum thrust curve is discrete for N number of point on point of each working time, adds in matrix (2), then in formula (2)
Sample point number becomes 2m+k from N number of；
6) convergence judges: when iteration is initial, make iterations k=0, it is intended that search precision eps and maximum search step number Kmax,
The mean-squared departure of any two the most corresponding thrust curves of different discrete instants it is iterated being calculated according to formula (5):
Wherein, F2m+k-1(ti) it is the thrust curve at the 2m+k-1 discrete point, F2m+k(ti) it is the 2m+k discrete point
The thrust curve at place, F0(ti) it is the thrust curve of design objective requirement, N is discrete point number；
If error (k) is < eps or k=Kmax, then stop search, export design variable optimal solution Xk+2mAnd correspondence is
Excellent thrust curve f2m+k(t), [X2m+k,f2m+k(t)], otherwise, go to step 4) until iteration stopping when meeting this condition.
Solid propellant rocket motor charge method for designing the most according to claim 1, it is characterised in that described search precision eps refers to
It is set to 0.001.
Solid propellant rocket motor charge method for designing the most according to claim 1, it is characterised in that described maximum search step number Kmax
It is appointed as 5m.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100004769A1 (en) * | 2008-07-01 | 2010-01-07 | Airbus Operations Ltd | Method of designing a structure |
| CN101944141A (en) * | 2010-08-18 | 2011-01-12 | 北京理工大学 | High-efficiency global optimization method using adaptive radial basis function based on fuzzy clustering |
| CN102682173A (en) * | 2012-05-13 | 2012-09-19 | 北京理工大学 | Optimization design method based on self-adaptive radial basis function surrogate model for aircraft |
| CN103955557A (en) * | 2014-03-31 | 2014-07-30 | 北京航空航天大学 | Multi-disciplinary integrated design optimization method and system for carrier rocket |
| CN105930562A (en) * | 2016-04-13 | 2016-09-07 | 浙江大学 | Structural performance optimum design method under non-probability conditions |
-
2016
- 2016-05-05 CN CN201610293080.3A patent/CN105956281B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100004769A1 (en) * | 2008-07-01 | 2010-01-07 | Airbus Operations Ltd | Method of designing a structure |
| CN101944141A (en) * | 2010-08-18 | 2011-01-12 | 北京理工大学 | High-efficiency global optimization method using adaptive radial basis function based on fuzzy clustering |
| CN102682173A (en) * | 2012-05-13 | 2012-09-19 | 北京理工大学 | Optimization design method based on self-adaptive radial basis function surrogate model for aircraft |
| CN103955557A (en) * | 2014-03-31 | 2014-07-30 | 北京航空航天大学 | Multi-disciplinary integrated design optimization method and system for carrier rocket |
| CN105930562A (en) * | 2016-04-13 | 2016-09-07 | 浙江大学 | Structural performance optimum design method under non-probability conditions |
Non-Patent Citations (1)
| Title |
|---|
| 武泽平 等: "应用径向基代理模型实现序列自适应再采样优化策略", 《国防科技大学学报》 * |
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